Results 1 to 10 of about 257,684 (210)

Elastic Net Regularization Paths for All Generalized Linear Models

Journal of Statistical Software, 2023
The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least ...
J. Kenneth Tay, Balasubramanian Narasimhan, Trevor Hastie   +2 more
doaj   +1 more source

A Lasso approach to covariate selection and average treatment effect estimation for clustered RCTs using design-based methods

Journal of Causal Inference, 2022
Statistical power is often a concern for clustered randomized control trials (RCTs) due to variance inflation from design effects and the high cost of adding study clusters (such as hospitals, schools, or communities).
Schochet Peter Z.
doaj   +1 more source

The Accessible Lasso Models

, 2016
A new line of research on the lasso exploits the beautiful geometric fact that the lasso fit is the residual from projecting the response vector $y$ onto a certain convex polytope.
Harris, Naftali, Sepehri, Amir
core   +1 more source

Corrigendum to: Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank [PDF]

, 2021
Ruilin Li, Christopher Chang, Johanne Marie Justesen, Yosuke Tanigawa, Junyang Qian, Trevor Hastie, Manuel A. Rivas, Robert Tibshirani   +7 more
openalex   +1 more source

A Novel Clinical Prognostic Model for Breast Cancer Patients with Malignant Pleural Effusion: Avoiding Chemotherapy in Low-Risk Groups?

Cancer Management and Research, 2023
Yichen Wang,1,* Tao Zhou,2,* Shanshan Zhao,1,* Ning Li,3 Siwen Sun,1 Man Li1 1Department of Oncology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, People’s Republic of China; 2Department of Oncology, The First ...
Wang Y, Zhou T, Zhao S, Li N, Sun S, Li M   +5 more
doaj  

MARS via LASSO

The Annals of Statistics
Multivariate adaptive regression splines (MARS) is a popular method for nonparametric regression introduced by Friedman in 1991. MARS fits simple nonlinear and non-additive functions to regression data. We propose and study a natural lasso variant of the MARS method.
Ki, Dohyeong, Fang, Billy, Guntuboyina, Adityanand   +2 more
openaire   +3 more sources

Prediction of Taxi-in Time and Analysis of Influencing Factors for Arrival Flights at Airport with a Decentralised Terminal Layout

Promet (Zagreb)
Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness.
Xiaowei TANG, Mengfan YE, Shengrun ZHANG, Kurt FUELLHART   +3 more
doaj   +1 more source

A Comparison of Variables Selection Methods and their Sequential Application: A Case Study of the Bankruptcy of Polish Companies

Folia Oeconomica Stetinensia, 2020
Research background: Even though in recent decades, a lot of new techniques were developed, there is still a lack of studies aimed at comparing the performance of variable selection methods.
Zanka Mikhail
doaj   +1 more source

Prognostic Model for the Risk Stratification of Early and Late Recurrence in Hepatitis B Virus-Related Small Hepatocellular Carcinoma Patients with Global Histone Modifications

Journal of Hepatocellular Carcinoma, 2021
Jin-Ling Duan,1,* Run-Cong Nie,1,2,* Zhi-Cheng Xiang,1,3,* Jie-Wei Chen,3 Min-Hua Deng,1,2 Hu Liang,1,4 Feng-Wei Wang,1 Rong-Zhen Luo,3 Dan Xie,1,3 Mu-Yan Cai1,3 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer
Duan JL, Nie RC, Xiang ZC, Chen JW, Deng MH, Liang H, Wang FW, Luo RZ, Xie D, Cai MY   +9 more
doaj  

Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets

Entropy
We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions.
Ryan A. Peterson, Max McGrath, Joseph E. Cavanaugh   +2 more
doaj   +1 more source